diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..bed0738c7eeb449bca98b5d2f33c89a1ee56349a --- /dev/null +++ b/.gitattributes @@ -0,0 +1,60 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.avro filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.lz4 filter=lfs diff=lfs merge=lfs -text +*.mds filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +# Audio files - uncompressed +*.pcm filter=lfs diff=lfs merge=lfs -text +*.sam filter=lfs diff=lfs merge=lfs -text +*.raw filter=lfs diff=lfs merge=lfs -text +# Audio files - compressed +*.aac filter=lfs diff=lfs merge=lfs -text +*.flac filter=lfs diff=lfs merge=lfs -text +*.mp3 filter=lfs diff=lfs merge=lfs -text +*.ogg filter=lfs diff=lfs merge=lfs -text +*.wav filter=lfs diff=lfs merge=lfs -text +# Image files - uncompressed +*.bmp filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.tiff filter=lfs diff=lfs merge=lfs -text +# Image files - compressed +*.jpg filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text +*.webp filter=lfs diff=lfs merge=lfs -text +# Video files - compressed +*.mp4 filter=lfs diff=lfs merge=lfs -text +*.webm filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..dbf68c7530a15c4a733ddd926c14dcd7b817d7fe --- /dev/null +++ b/README.md @@ -0,0 +1,484 @@ +--- +license: cc-by-nc-4.0 +pretty_name: DosePET - An Open-Source Dataset with 2100 Radiation Dose Maps for PET/CT +tags: + - medical-imaging + - dosimetry + - pet-ct + - monte-carlo + - nuclear-medicine + - radiation-dose +size_categories: + - 1K The per-organ dose statistics +> for all 2100 studies are a 32 MB download, and are also browsable in the +> Dataset Viewer above. See [Quick start](#quick-start). + +--- + +## Quick start + +```bash +pip install huggingface_hub # recommended, not required +wget https://huggingface.co/datasets/eltzanis/DosePET/resolve/main/download_dataset.py + +python3 download_dataset.py list # what's available + +python3 download_dataset.py get --content tables --out ./petct # 32 MB +python3 download_dataset.py get --sample 5 --out ./petct # 5 full studies +python3 download_dataset.py get --subset PSMA --out ./petct # one tracer +python3 download_dataset.py get --all --out ./petct # everything + +python3 download_dataset.py get --all --dry-run # size check, no download +``` + +Every selection extracts into the same layout, so you can start small and add +more later without reorganising anything. + +--- + +## Downloading exactly what you need + + +### 1. Only the organ dose tables — 32 MB + +Every `dose_report.csv`, `metadata.json`, `dosimetry_config.json` and +`segmentation/statistics.json`, for all 2100 studies. No dose images, no masks. + +```bash +python3 download_dataset.py get --content tables --out ./petct +``` + +The same data is +in `metadata/dose_reports.parquet` (24 MB, 245,700 rows) if you would rather +have one table than 2100 files: + +```python +import pandas as pd +df = pd.read_parquet("hf://datasets/eltzanis/DosePET/metadata/dose_reports.parquet") +df[df.organ == "liver"].groupby("isotope_key").mean_dose_rate_at_scan_mGy_per_h.describe() +``` + +### 2. Named studies — only their bytes are fetched + +```bash +# find the ids +python3 download_dataset.py list --cases + +# fetch three of them, from three different shards +python3 download_dataset.py get \ + --cases FDG/0001,FDG/0500,PSMA/PSMA_0179419e313f7d8c/05-03-2002-NA-PETCT_whole-body_PSMA-40379 \ + --out ./petct +``` + +The dose shards are uncompressed tars and the manifest records each study's +byte offset, so the script issues HTTP Range requests and pulls only what you +asked for: + +``` +range-fetching 1 case(s), 144.04 MB of 9.91 GB +range-fetching 1 case(s), 133.40 MB of 9.94 GB +range-fetching 1 case(s), 105.14 MB of 9.94 GB +done: 366 files +``` + +For a long list use a file: + +```bash +printf 'FDG/0001\nFDG/0002\nFDG/0500\n' > my_cases.txt +python3 download_dataset.py get --case-list my_cases.txt --out ./petct +``` + +Add `--no-range` to download whole shards instead (useful behind a proxy that +strips Range headers; the script falls back to this automatically anyway). + +### 3. A representative sample + +```bash +python3 download_dataset.py get --sample 10 --out ./petct +``` + +Ten studies spread evenly across the dataset rather than ten consecutive ids. +Combine with `--subset` to sample one tracer: + +```bash +python3 download_dataset.py get --sample 10 --subset PSMA --out ./petct +``` + +### 4. Masks and tables but no dose images + +```bash +python3 download_dataset.py get --content tables,seg --out ./petct +``` + +All 245,700 AI generted organ masks for all 2100 studies: **5.3 GB** + +### 5. One tracer + +```bash +python3 download_dataset.py get --subset FDG --out ./petct # 1503 studies +python3 download_dataset.py get --subset PSMA --out ./petct # 597 studies +``` + +### 6. By shard, for batching a big transfer + +```bash +python3 download_dataset.py get --subset FDG --shards 0-3 --out ./petct +python3 download_dataset.py get --subset FDG --shards 4-7 --out ./petct +``` + +`FDG_dose_007.tar` and `FDG_seg_007.tar.gz` hold exactly the same studies, so a +shard index always gives you complete studies. + +### 7. Everything + +```bash +python3 download_dataset.py get --all --out ./petct +``` + +### Always check first + +```bash +python3 download_dataset.py get --all --dry-run +python3 download_dataset.py get --cases FDG/0001,FDG/0002 --dry-run +``` + +`--dry-run` prints the exact download size and the shards involved, and +downloads nothing. + +### Measured download sizes + +| what you ask for | download | +|---|---| +| `--content tables` | 32 MB | +| one named study | 333 MB | +| `--sample 5` | 1.46 GB | +| `--sample 10` | 2.80 GB | +| `--sample 50` | 13.5 GB | +| `--content tables,seg` (all masks, no dose) | 5.32 GB | +| `--subset PSMA` | 68.2 GB | +| `--subset FDG` | 314.7 GB | +| `--all` | 382.8 GB | + +A named study costs more than its dose image alone (175 MB) because the masks +for its shard arrive as one 150 MB gzipped archive and the 32 MB tables shard +comes with it. + +--- + +## Coming back for more later + +**Point `--out` at the same directory and the dataset grows in place.** Nothing +is re-downloaded, nothing already on disk is touched. + +```bash +python3 download_dataset.py get --content tables --out ./petct # today: 32 MB +python3 download_dataset.py get --cases FDG/0001 --out ./petct # later: 333 MB +python3 download_dataset.py get --sample 50 --out ./petct # later: 13.5 GB +python3 download_dataset.py get --all --out ./petct # finally: 383 GB +``` + + +Two things worth knowing: + +- Each archive is **deleted after it is extracted**, to save disk. If a + download is interrupted mid-shard it restarts that shard; pass + `--keep-archives` to keep them and make interruptions cheaper. +- The tables shard (32 MB) covers all 2100 studies, so it comes down again + with each new batch. Harmless, but it is why a "three more studies" batch + reports ~867 MB rather than ~836 MB. + +--- + +## What you end up with + +The extracted tree is **identical to the source dataset** — same directory +structure, same filenames, same bytes. Verified by extracting a complete shard +pair and diffing it against the original: + +``` +directory tree : IDENTICAL - 2976 paths, same names, same nesting +file contents : ALL 2928 FILES BYTE-IDENTICAL +``` + +Partial downloads give you a correct subtree: take only the +tables and you get `FDG/0001/dose_report.csv` in its proper place with +`dose_image.nii.gz` simply absent. Add masks or dose images later and they land +in the same tree. + +The one thing not preserved is **file modification times** — they are set to +when you extracted, not when the data was produced. File contents are bit-for-bit +identical. + +### Integrity + +Every archive's SHA-256 is checked against `shards.json` before extraction, and +the download aborts on a mismatch rather than extracting corrupt data. To +re-check archives you kept with `--keep-archives`: + +```bash +python3 download_dataset.py verify --cache ./petct_cache +``` + +--- + +## What each study contains + +``` +FDG/0001/ + dose_image.nii.gz total absorbed dose, Gy, uint16 + scl_slope + dose_report.csv 117 rows, per-organ dose statistics + metadata.json isotope, half-life, injected activity, timings + dosimetry_config.json cumulated activity, primaries, integration mode + segmentation/ + liver.nii.gz ... 117 binary organ masks (TotalSegmentator) + statistics.json mask volumes + +PSMA/// same files, one extra directory level +``` + +### `dose_image.nii.gz` + +Each voxel is the **total absorbed dose in Gy**, integrated from the start of +the scan to infinity assuming **physical decay only** — the activity +distribution is frozen in space, with no biological clearance and no +redistribution. + +Stored as `uint16` with a `scl_slope` in the NIfTI header (quantisation step = +`max_dose / 65535`, about 1.5e-5 relative). **You must apply `scl_slope`.** +`nibabel` and `SimpleITK` do this automatically: + +```python +import nibabel as nib, numpy as np +dose_Gy = np.asanyarray(nib.load("FDG/0001/dose_image.nii.gz").dataobj) # Gy +``` + +### `dose_report.csv` + +One row per organ, alphabetical: + +| column | meaning | +|---|---| +| `organ` | TotalSegmentator label | +| `mean_dose_Gy`, `max_dose_Gy`, `min_dose_Gy`, `dose_std_Gy` | dose in the mask | +| `mean_dose_injection_Gy`, `max_dose_injection_Gy` | back-extrapolated to injection time | +| `volume_cm3`, `mass_g`, `num_voxels` | mask geometry | +| `dcf_Gy_per_Bqs` | dose conversion factor, Gy per decay in the FOV | +| `mean_dose_rate_at_scan_mGy_per_s`, `mean_dose_rate_at_scan_mGy_per_h` | dose rate at scan start | + +All 245,700 rows are also published as `metadata/dose_reports.parquet` for the +Dataset Viewer, joined with each study's isotope, half-life, injected activity +and cumulated activity. + +In the Parquet table the dose conversion factor is given as +**`dcf_fGy_per_Bqs`** — the same quantity as the CSVs' `dcf_Gy_per_Bqs`, in +femtogray (multiplied by 10^15^). Doses back-extrapolated to injection time are provided in +each study's `dose_report.csv`, and simulation parameters in its +`dosimetry_config.json`. + +A zero in `dcf_fGy_per_Bqs` (8857 rows) means the organ mask is empty because +the structure lies outside the acquired field of view, not that the value is +missing. + +--- + +## Deriving dose rate and Gy-per-decay + +Because the only time dependence is a single exponential shared by every voxel, +both quantities are the dose image times **one scalar per study**: + +``` +lambda = ln2 / T_half T_half from metadata.json +rate = D * 3.6e6 * lambda [mGy/h] at scan start +DCF = D / A_tilde [Gy per decay] A_tilde = cumulated_from_scan +``` + +```python +import json, math, numpy as np, nibabel as nib +case = "FDG/0001" +cfg = json.load(open(f"{case}/dosimetry_config.json")) +meta = json.load(open(f"{case}/metadata.json")) +T_half = cfg.get("half_life_seconds") or meta["isotope"]["half_life_seconds"] +A_tilde = cfg["cumulated_from_scan"] + +D = np.asanyarray(nib.load(f"{case}/dose_image.nii.gz").dataobj) # Gy +rate_mGy_per_h = D * 3.6e6 * math.log(2) / T_half +dcf_Gy_per_dec = D / A_tilde +``` + +For organ-level numbers you need none of this — the columns are already in +`dose_report.csv`. + +### `tools/convert_dose_images.py` + +It does the conversions above for you, and can +independently re-verify the published tables. + +```bash +DATA=./petct # where you extracted the dataset + +# voxel-wise dose-rate maps, mGy/h at scan start +python3 tools/convert_dose_images.py rate --root $DATA/FDG --out ./rate_maps + +# voxel-wise Gy-per-decay maps +python3 tools/convert_dose_images.py dcf --root $DATA/PSMA --out ./dcf_maps + +# per-organ table straight from the image + masks, in the same column +# order as dose_report.csv so the two can be diffed +python3 tools/convert_dose_images.py organs --case $DATA/FDG/0001 --out table.csv + +# recompute every reproducible column and compare against the published CSV +python3 tools/convert_dose_images.py check --root $DATA/FDG +``` + +Other time points are opt-in: `--at injection` (uses the stored injection +correction factor) or `--at +3600` for one hour after the scan. Units via +`--unit mGy_per_h | mGy_per_s | Gy_per_s`. + +**The conversion is lossless.** By default the tool copies the `uint16` payload +byte for byte and multiplies only the NIfTI `scl_slope`, so the output carries +no extra quantisation and is the same size as the input. Use `--mode float32` +if your software ignores `scl_slope`. + +`check` is the reproducibility anchor: it recomputes organ means from the dose +image and the masks, applies the scale factor, and diffs against the shipped +`dose_report.csv`, printing PASS/FAIL per study: + +``` +[1/2] PASS 0001 117 organs, 1283 values, worst 0.499 x tolerance +``` + +Full usage in `tools/README.md`. Requires `numpy`, `nibabel`, and `SimpleITK` +for the mask-based commands. + +> Converting a whole subset duplicates it on disk (hundreds of GB). Unless you +> need the files on disk, apply the scalar on the fly with the six lines above. + +--- + +## How the release is packaged + +Three kinds of shard, all tar archives whose members keep their original +relative paths: + +| shard | contents | compression | why | +|---|---|---|---| +| `data/tables.tar.gz` | every `.csv` / `.json`, all 2100 studies | gzip | 32 MB, so table-only users download almost nothing | +| `data//_dose_NNN.tar` | `dose_image.nii.gz` | **none** | already gzipped NIfTI | +| `data//_seg_NNN.tar.gz` | organ masks | gzip | masks compress about **23:1** (115 GB → 5 GB) | + +`*_dose_NNN` and `*_seg_NNN` with the same `NNN` hold **exactly the same +studies**, so a complete slice of the dataset is two downloads. + +Alongside the data: + +| path | what | +|---|---| +| `download_dataset.py` | the download script | +| `shards.json` | manifest: sizes, SHA-256, case lists, per-case byte offsets | +| `metadata/dose_reports.parquet` | all 245,700 organ rows, for the Dataset Viewer | +| `tools/convert_dose_images.py` | dose-rate / Gy-per-decay conversion + verification | +| `tools/README.md` | usage guide for the conversion tool | +| `skills/dosepet-research/` | agent skill: instructions for AI agents working with the dataset | + +`shards.json` lists every shard with its size, SHA-256 and case list, and maps +each case id to its shards. The download script verifies every archive against +that checksum before extracting. + +--- + +## Using DosePET with AI agents + +`skills/dosepet-research/` is an agent skill — a set of instructions that an +agentic AI system (for example Claude Code or OpenAI Codex) can load to work +with this dataset correctly. It covers selective downloading, retrieving and +matching the source PET/CT examinations from ENHANCE.PET and +PSMA-PET-CT-Lesions, verifying spatial correspondence before use, and the +physical meaning and units of the dose quantities. + +```bash +hf download eltzanis/DosePET --repo-type dataset --include "skills/*" --local-dir . +``` + +Then make the `skills/dosepet-research` folder available to your agent (for +Claude Code, copy it into `~/.claude/skills/` or the project's +`.claude/skills/`). + +--- + +## Assumptions and caveats + +1. **Physical decay only.** No biological clearance or redistribution. +2. **Time zero is scan start.** +3. **`dcf_Gy_per_Bqs` is normalised to decays in the imaged FOV**, with that + patient's own tracer distribution as the source. It is a FOV-normalised + S-value, *not* dose per decay in a specific source organ, and should not be + compared directly with MIRD/OLINDA organ-to-organ S-values. +4. **Apply `scl_slope`** (if it is not applied automatically) when reading the dose images. +5. **Four FDG studies** (`0718`, `1022`, `1195`, `1289`) have no injection + correction factor, because the acquisition time is missing upstream. Their + `dose_report.csv` therefore omits `mean_dose_injection_Gy` and + `max_dose_injection_Gy`. Scan-time dose, dose rate and DCF are unaffected. + +--- + + + +## Citation + +If you use DosePET in your research, please cite: + +```bibtex +@article{Tzanis2026DosePET, + title = {DosePET: An Open-Source Dataset with 2100 Radiation Dose Maps for PET/CT}, + author = {Eleftherios Tzanis and Antonios Tzortzakakis and Michail E. Klontzas}, + year = {2026}, + note = {Manuscript under review} +} +``` + + +## License + +**CC BY-NC 4.0** — Creative Commons Attribution-NonCommercial 4.0 +International. You may share and adapt this dataset for **non-commercial** +purposes with attribution. See https://creativecommons.org/licenses/by-nc/4.0/ + +The underlying PET/CT images come from their respective source collections; +please also honour their terms and cite them. + +- **FDG subset** — ENHANCE.PET whole-/total-body [18F]FDG-PET/CT dataset: + + Ferrara D, Pires M, Gutschmayer S, et al. Sharing a whole-/total-body + [18F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET + initiative. *Sci Data* 13, 869 (2026). + https://doi.org/10.1038/s41597-026-07218-y + +- **PSMA subset** — LMU Munich whole-body PSMA-PET/CT dataset: + + Jeblick K, Schachtner B, Mittermeier A, et al. 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sha256:efcdaac30be4eb8f1a90147b8f7fa56e539ce6e04a56b51ce088a171ed476742 +size 125538165 diff --git a/data/tables.tar.gz b/data/tables.tar.gz new file mode 100644 index 0000000000000000000000000000000000000000..b796e01065dce525dcc412b661dc673503ca7f80 --- /dev/null +++ b/data/tables.tar.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c547e59a5079e14210eb7576c79d80b1ea612a6b9bcee40913a7c4c53933d72e +size 31121036 diff --git a/download_dataset.py b/download_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..b5cc9110615955e66dcea8a4248dcb26edd1ac2b --- /dev/null +++ b/download_dataset.py @@ -0,0 +1,614 @@ +#!/usr/bin/env python3 +""" +download_dataset.py — download all or part of the PET/CT Monte Carlo organ +dosimetry dataset from the HuggingFace Hub. + +The dataset is published as shards. Each shard is a tar archive whose +members keep their original relative paths, so extracting shards into one +directory reproduces the published tree: + + /FDG/0001/dose_image.nii.gz + /FDG/0001/dose_report.csv + /FDG/0001/segmentation/liver.nii.gz + /PSMA///dose_image.nii.gz + +There are three kinds of shard: + + tables one small archive (31 MB) with dose_report.csv, + metadata.json, dosimetry_config.json and + segmentation/statistics.json for ALL cases + dose dose_image.nii.gz, plain tar (already-compressed NIfTI) + seg segmentation masks, gzipped tar (about 23:1) + +Dose and seg shards with the same index hold exactly the same cases. + +Examples + # what is available, and how big + download_dataset.py list + + # just the organ dose tables for every case (31 MB) + download_dataset.py get --content tables --out ./petct + + # a 5-case sample, complete (dose + masks + tables) + download_dataset.py get --sample 5 --out ./petct + + # named cases only, fetching just their bytes out of the shards + download_dataset.py get --cases FDG/0001,FDG/0002 --out ./petct + + # everything for one tracer + download_dataset.py get --subset PSMA --out ./petct + + # masks and tables but no dose images + download_dataset.py get --content tables,seg --out ./petct + + # the whole dataset + download_dataset.py get --all --out ./petct + + # see the download size without downloading + download_dataset.py get --all --dry-run + +Requirements + pip install huggingface_hub (recommended: resumable, cached) + or nothing at all - the script falls back to urllib over https. +""" + +from __future__ import annotations + +import argparse +import hashlib +import io +import json +import os +import shutil +import sys +import tarfile +import time +import urllib.error +import urllib.request +from pathlib import Path +from typing import Dict, Iterable, List, Optional, Set, Tuple + +# -------------------------------------------------------------------------- +# Where the data lives. Edit REPO_ID if you mirror the dataset elsewhere. +# -------------------------------------------------------------------------- +REPO_ID = os.environ.get("PETCT_REPO_ID", "eltzanis/DosePET") +REVISION = os.environ.get("PETCT_REVISION", "main") +MANIFEST_NAME = "shards.json" + +# Point this at a local copy of the release (or a mounted mirror) to work +# entirely offline; the script then copies instead of downloading. +LOCAL_ROOT = os.environ.get("PETCT_LOCAL_ROOT", "") + +GB = 1024 ** 3 +MB = 1024 ** 2 + + +def _url(filename: str) -> str: + return (f"https://huggingface.co/datasets/{REPO_ID}/resolve/" + f"{REVISION}/{filename}") + + +def human(n: float) -> str: + for unit, size in (("TB", GB * 1024), ("GB", GB), ("MB", MB), ("KB", 1024)): + if abs(n) >= size: + return f"{n / size:.2f} {unit}" + return f"{n:.0f} B" + + +# -------------------------------------------------------------------------- +# Transport +# -------------------------------------------------------------------------- + +def _hf_api(): + try: + from huggingface_hub import hf_hub_download # noqa: F401 + return True + except ImportError: + return False + + +def fetch(filename: str, cache: Path, expect_size: Optional[int] = None, + quiet: bool = False) -> Path: + """ + Make repo file `filename` available at cache/filename and return that path. + + Order of preference: a local mirror (PETCT_LOCAL_ROOT), then + huggingface_hub if installed (better retries and caching), then plain + HTTPS with a Range header so an interrupted download resumes. + """ + dest = cache / filename + dest.parent.mkdir(parents=True, exist_ok=True) + if expect_size and dest.exists() and dest.stat().st_size == expect_size: + if not quiet: + print(f" have {filename}") + return dest + + if LOCAL_ROOT: + src = Path(LOCAL_ROOT).expanduser() / filename + if not src.is_file(): + raise FileNotFoundError(f"{src} not found (PETCT_LOCAL_ROOT is set)") + if not quiet: + print(f" copy {filename} ({human(src.stat().st_size)})") + try: # hardlink when on one filesystem + os.link(src, dest) + except OSError: + shutil.copyfile(src, dest) + return dest + + if _hf_api(): + from huggingface_hub import hf_hub_download + got = Path(hf_hub_download(repo_id=REPO_ID, repo_type="dataset", + revision=REVISION, filename=filename, + local_dir=str(cache))) + if got.resolve() != dest.resolve(): + if not dest.exists(): + try: + os.link(got, dest) + except OSError: + shutil.copy2(got, dest) + return dest + + url = _url(filename) + part = dest.with_suffix(dest.suffix + ".part") + have = part.stat().st_size if part.exists() else 0 + req = urllib.request.Request(url) + if have: + req.add_header("Range", f"bytes={have}-") + t0 = time.time() + try: + with urllib.request.urlopen(req) as resp, open(part, "ab" if have else "wb") as fh: + total = int(resp.headers.get("Content-Length", 0)) + have + done = have + while True: + chunk = resp.read(4 * MB) + if not chunk: + break + fh.write(chunk) + done += len(chunk) + if not quiet and total: + pct = 100.0 * done / total + rate = done / max(time.time() - t0, 1e-6) + sys.stdout.write(f"\r {filename} {pct:5.1f}% " + f"{human(done)}/{human(total)} " + f"{human(rate)}/s ") + sys.stdout.flush() + except urllib.error.HTTPError as exc: + if exc.code == 416 and have: # already complete + pass + else: + raise + if not quiet: + sys.stdout.write("\n") + os.replace(part, dest) + return dest + + +def fetch_range(filename: str, offset: int, length: int) -> Optional[bytes]: + """ + Pull just bytes [offset, offset+length) of a repo file. Returns None if + ranges are unavailable, so the caller can fall back to a whole download. + + This is what makes "give me these 5 cases" cheap: an uncompressed tar is a + concatenation of 512-byte-aligned header+data blocks, so one case's bytes + can be fetched out of a 10 GB shard without touching the rest. + """ + if LOCAL_ROOT: + src = Path(LOCAL_ROOT).expanduser() / filename + if not src.is_file(): + return None + with open(src, "rb") as fh: + fh.seek(offset) + return fh.read(length) + + req = urllib.request.Request(_url(filename)) + req.add_header("Range", f"bytes={offset}-{offset + length - 1}") + try: + with urllib.request.urlopen(req) as resp: + if resp.status != 206: + return None + return resp.read() + except (urllib.error.HTTPError, urllib.error.URLError): + return None + + +def extract_tar_slice(blob: bytes, out: Path) -> int: + """ + Treat a byte slice cut out of a tar as a standalone tar stream: append the + two zero blocks that mark end-of-archive and read it normally. + """ + stream = io.BytesIO(blob + b"\0" * 1024) + n = 0 + with tarfile.open(fileobj=stream, mode="r:") as tf: + for m in safe_members(tf, out): + tf.extract(m, path=out, set_attrs=False) + if m.isfile(): + n += 1 + return n + + +def get_cases_by_range(man: Dict, shard: Dict, cases: Set[str], + out: Path) -> Optional[int]: + """ + Extract only `cases` from an uncompressed shard using Range requests. + Returns the number of files written, or None if this is not possible + (compressed shard, missing offsets, or a server that refuses ranges). + """ + if shard["compressed"]: + return None + wanted = [c for c in shard["cases"] if c in cases] + ranges = [] + for c in wanted: + rng = man["cases"].get(c, {}).get("dose_range") + if not rng: + return None + ranges.append((c, rng[0], rng[1])) + if not ranges: + return 0 + + total = sum(n for _, _, n in ranges) + print(f" range-fetching {len(ranges)} case(s), {human(total)} " + f"of {human(shard['size_bytes'])}") + written = 0 + for c, offset, length in ranges: + blob = fetch_range(shard["name"], offset, length) + if blob is None: + print(" server refused Range requests; " + "falling back to whole shard") + return None + if len(blob) != length: + print(f" short range read for {c}; falling back") + return None + written += extract_tar_slice(blob, out) + return written + + +# -------------------------------------------------------------------------- +# Manifest +# -------------------------------------------------------------------------- + +def load_manifest(cache: Path, refresh: bool = False) -> Dict: + local = cache / MANIFEST_NAME + if refresh or not local.exists(): + try: + fetch(MANIFEST_NAME, cache, quiet=True) + except Exception as exc: + if not local.exists(): + sys.exit(f"could not fetch {MANIFEST_NAME} from {REPO_ID}: {exc}\n" + f"If you mirror this dataset, set PETCT_REPO_ID.") + return json.loads(local.read_text()) + + +def select_shards(man: Dict, args) -> Tuple[List[Dict], Set[str]]: + """ + Turn the user's flags into a concrete shard list, plus the set of cases + they asked for (empty means "whole shards"). + """ + kinds = set(k.strip() for k in args.content.split(",")) if args.content \ + else {"tables", "dose", "seg"} + shards = man["shards"] + + if args.subset: + wanted = set(s.strip() for s in args.subset.split(",")) + shards = [s for s in shards + if s["subset"] in wanted or s["kind"] == "tables"] + + cases: Set[str] = set() + if args.cases: + cases = set(c.strip() for c in args.cases.split(",") if c.strip()) + if args.case_list: + cases |= set(l.strip() for l in Path(args.case_list).read_text().split() + if l.strip()) + if args.sample: + pool = sorted(man["cases"]) + if args.subset: + wanted = set(s.strip() for s in args.subset.split(",")) + pool = [c for c in pool if man["cases"][c]["subset"] in wanted] + step = max(len(pool) // args.sample, 1) + cases = set(pool[::step][:args.sample]) + + unknown = sorted(c for c in cases if c not in man["cases"]) + if unknown: + sys.exit(f"unknown case id(s): {unknown[:5]}\n" + f"Run `download_dataset.py list --cases` to see valid ids.") + + if cases: + keep = set() + for c in cases: + info = man["cases"][c] + keep.add(info["dose_shard"]) + keep.add(info["seg_shard"]) + shards = [s for s in shards + if s["name"] in keep or s["kind"] == "tables"] + + if args.shards: + idx = parse_indices(args.shards) + shards = [s for s in shards + if s["kind"] == "tables" + or int(s["name"].rsplit("_", 1)[1].split(".")[0]) in idx] + + shards = [s for s in shards if s["kind"] in kinds] + return shards, cases + + +def parse_indices(spec: str) -> Set[int]: + out: Set[int] = set() + for part in spec.split(","): + part = part.strip() + if not part: + continue + if "-" in part: + a, b = part.split("-", 1) + out.update(range(int(a), int(b) + 1)) + else: + out.add(int(part)) + return out + + +# -------------------------------------------------------------------------- +# Extraction +# -------------------------------------------------------------------------- + +def safe_members(tf: tarfile.TarFile, out: Path, + keep: Optional[Set[str]] = None) -> Iterable[tarfile.TarInfo]: + """ + Yield members that are regular files inside out. Refuses absolute paths, + parent traversal, symlinks and devices, so a hostile archive cannot + write outside the target directory. + """ + root = out.resolve() + for m in tf: + if not (m.isfile() or m.isdir()): + continue # skip symlinks, devices, hardlinks + name = m.name + if name.startswith("/") or ".." in Path(name).parts: + print(f" refusing suspicious member {name}") + continue + target = (root / name).resolve() + if target != root and root not in target.parents: + print(f" refusing out-of-tree member {name}") + continue + if keep is not None and m.isfile(): + case = case_of(name) + if case not in keep: + continue + yield m + + +def case_of(member: str) -> str: + """ + The case id a tar member belongs to: FDG/ or PSMA//. + """ + parts = Path(member).parts + if not parts: + return "" + if parts[0] == "PSMA" and len(parts) >= 3: + return "/".join(parts[:3]) + if len(parts) >= 2: + return "/".join(parts[:2]) + return parts[0] + + +def extract(path: Path, out: Path, compressed: bool, + keep: Optional[Set[str]] = None) -> int: + mode = "r:gz" if compressed else "r" + n = 0 + with tarfile.open(path, mode) as tf: + for m in safe_members(tf, out, keep): + tf.extract(m, path=out, set_attrs=False) + if m.isfile(): + n += 1 + return n + + +def sha256_file(path: Path, chunk: int = 8 * MB) -> str: + h = hashlib.sha256() + with open(path, "rb") as fh: + for block in iter(lambda: fh.read(chunk), b""): + h.update(block) + return h.hexdigest() + + +# -------------------------------------------------------------------------- +# Commands +# -------------------------------------------------------------------------- + +def cmd_list(args) -> int: + man = load_manifest(Path(args.cache), refresh=args.refresh) + tot = man["totals"] + print(f"repo : {REPO_ID} @ {REVISION}") + print(f"created : {man['created_utc']}") + print(f"cases : {tot['cases']} source files: {tot['files']}") + print(f"unpacked: {human(tot['dose_bytes'] + tot['seg_bytes'] + tot['table_bytes'])}") + print() + if args.cases: + for c in sorted(man["cases"]): + i = man["cases"][c] + print(f" {c:70s} shard {i['shard_index']:03d} " + f"dose {human(i['dose_bytes']):>9s} seg {human(i['seg_bytes']):>9s}") + return 0 + by_kind: Dict[str, List[Dict]] = {} + for s in man["shards"]: + by_kind.setdefault(s["kind"], []).append(s) + print(f"{'kind':8s} {'shards':>7s} {'download':>12s} {'unpacked':>12s}") + for kind in ("tables", "seg", "dose"): + ss = by_kind.get(kind, []) + if not ss: + continue + print(f"{kind:8s} {len(ss):7d} " + f"{human(sum(s['size_bytes'] for s in ss)):>12s} " + f"{human(sum(s['raw_bytes'] for s in ss)):>12s}") + print(f"{'TOTAL':8s} {len(man['shards']):7d} " + f"{human(sum(s['size_bytes'] for s in man['shards'])):>12s}") + print() + print("shards, by subset:") + for subset in sorted({s["subset"] for s in man["shards"]}): + ss = [s for s in man["shards"] if s["subset"] == subset] + print(f" {subset:6s} {len(ss):3d} files " + f"{human(sum(s['size_bytes'] for s in ss))} " + f"{sum(len(s['cases']) for s in ss if s['kind'] in ('dose','tables'))} cases") + return 0 + + +def cmd_get(args) -> int: + man = load_manifest(Path(args.cache), refresh=args.refresh) + if not (args.all or args.subset or args.cases or args.case_list + or args.sample or args.content or args.shards): + sys.exit("nothing selected. Use --all, or one of --subset / --cases / " + "--case-list / --sample / --shards / --content. " + "See --help for examples.") + + shards, cases = select_shards(man, args) + if not shards: + sys.exit("selection matched no shards.") + + use_range = bool(cases) and not args.whole_shards and not args.no_range + whole = sum(s["size_bytes"] for s in shards) + up = sum(s["raw_bytes"] for s in shards) + + if use_range: + # dose shards are fetched by byte range; the rest come down whole + ranged = sum(man["cases"][c].get("dose_range", [0, 0])[1] for c in cases) + other = sum(s["size_bytes"] for s in shards if s["kind"] != "dose") + dl = ranged + other + else: + dl = whole + + print(f"{len(shards)} shard(s) selected") + print(f" download : {human(dl)}") + if use_range and dl < whole: + print(f" (only the requested cases' bytes are fetched from " + f"the dose shards; whole shards would be {human(whole)})") + if cases: + print(f" cases : {len(cases)} requested " + f"(only these are extracted)") + else: + print(f" unpacked : {human(up)}") + for s in sorted(shards, key=lambda s: s["name"]): + print(f" {s['name']:34s} {human(s['size_bytes']):>10s} " + f"{len(s['cases'])} cases") + if args.dry_run: + print("\n--dry-run: nothing downloaded.") + return 0 + + out = Path(args.out).expanduser().resolve() + cache = Path(args.cache).expanduser().resolve() + out.mkdir(parents=True, exist_ok=True) + keep = cases if (cases and not args.whole_shards) else None + + n_files = 0 + for i, s in enumerate(sorted(shards, key=lambda s: s["name"]), 1): + print(f"\n[{i}/{len(shards)}] {s['name']}") + + # A handful of named cases out of a 10 GB shard: fetch only their + # bytes rather than the whole archive. + if keep and not args.no_range: + got = get_cases_by_range(man, s, keep, out) + if got is not None: + n_files += got + print(f" extracted {got} files without downloading the shard") + continue + + local = fetch(s["name"], cache, expect_size=s["size_bytes"]) + + if local.stat().st_size != s["size_bytes"]: + sys.exit(f" size mismatch for {s['name']}: expected " + f"{s['size_bytes']}, got {local.stat().st_size}") + if not args.no_verify: + print(" verifying sha256 ...", end="", flush=True) + digest = sha256_file(local) + if digest != s["sha256"]: + sys.exit(f"\n CHECKSUM MISMATCH for {s['name']}\n" + f" expected {s['sha256']}\n got {digest}\n" + f" Delete {local} and retry.") + print(" ok") + + print(" extracting ...", end="", flush=True) + got = extract(local, out, s["compressed"], keep) + n_files += got + print(f" {got} files") + + if not args.keep_archives: + local.unlink() + + print(f"\ndone: {n_files} files under {out}") + print("Layout: /FDG//... and /PSMA///...") + if not args.keep_archives: + print("(archives deleted after extraction; pass --keep-archives to retain them)") + return 0 + + +def cmd_verify(args) -> int: + """Re-check already-downloaded archives in the cache.""" + man = load_manifest(Path(args.cache), refresh=False) + cache = Path(args.cache).expanduser().resolve() + bad = seen = 0 + for s in man["shards"]: + p = cache / s["name"] + if not p.exists(): + continue + seen += 1 + digest = sha256_file(p) + ok = digest == s["sha256"] + print(f" {'ok ' if ok else 'SHA BAD '} {s['name']}") + bad += 0 if ok else 1 + print(f"\n{seen} archive(s) present, {bad} bad") + return 1 if bad else 0 + + +def main(argv=None) -> int: + ap = argparse.ArgumentParser( + description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + sub = ap.add_subparsers(dest="command", required=True) + + def common(sp): + sp.add_argument("--cache", default="./petct_cache", + help="where archives and the manifest are kept") + sp.add_argument("--refresh", action="store_true", + help="re-fetch the manifest") + + l = sub.add_parser("list", help="show what is available") + common(l) + l.add_argument("--cases", action="store_true", + help="list every case id and its shard") + + g = sub.add_parser("get", help="download and extract") + common(g) + g.add_argument("--out", default="./petct_dataset", + help="where the dataset tree is written") + g.add_argument("--all", action="store_true", help="the whole dataset") + g.add_argument("--subset", help="FDG, PSMA, or FDG,PSMA") + g.add_argument("--content", default="", + help="tables,dose,seg (default: all three)") + g.add_argument("--cases", help="comma-separated case ids, e.g. FDG/0001") + g.add_argument("--case-list", help="file with one case id per line") + g.add_argument("--sample", type=int, + help="N cases spread evenly across the dataset") + g.add_argument("--shards", help="shard indices, e.g. 0-3,7") + g.add_argument("--whole-shards", action="store_true", + help="with --cases, extract entire shards, not just those cases") + g.add_argument("--dry-run", action="store_true", + help="print the download size and stop") + g.add_argument("--keep-archives", action="store_true", + help="do not delete each archive after extracting it") + g.add_argument("--no-verify", action="store_true", + help="skip sha256 verification (not recommended)") + g.add_argument("--no-range", action="store_true", + help="with --cases, download whole shards instead of " + "Range-fetching just those cases") + + v = sub.add_parser("verify", help="re-check archives already in the cache") + common(v) + + args = ap.parse_args(argv) + if args.command == "list": + return cmd_list(args) + if args.command == "get": + return cmd_get(args) + if args.command == "verify": + return cmd_verify(args) + return 2 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/metadata/dose_reports.parquet b/metadata/dose_reports.parquet new file mode 100644 index 0000000000000000000000000000000000000000..2a65982294338d15d929d7f25a24b2a32d1e1c44 --- /dev/null +++ b/metadata/dose_reports.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4729c4660eab1f450c68d5cd53ebe2aacd280b8e612dab5da2df0e83334598c1 +size 18643094 diff --git a/shards.json b/shards.json new file mode 100644 index 0000000000000000000000000000000000000000..205af18409b9f5d8b56b362ff4cf0e769d02f6f8 --- /dev/null +++ b/shards.json @@ -0,0 +1,32491 @@ +{ + "dataset": "PET/CT Monte Carlo organ dosimetry", + "manifest_version": 1, + "created_utc": "2026-10-13T14:30:31Z", + "source_root_layout": "// for FDG, /// for PSMA", + "extraction": "extract every shard into one directory to reproduce the published tree; all members use original relative paths", + "shard_target_gb": 10.0, + "totals": { + "cases": 2100, + "dose_bytes": 405316219429, + "seg_bytes": 141034415045, + "table_bytes": 80145508, + "files": 256200 + }, + "shards": [ + { + "name": "data/FDG/FDG_dose_000.tar", + "kind": "dose", + "subset": "FDG", + "compressed": false, + "size_bytes": 10636636160, + "sha256": "832d813deb9aad31f6b69451d53a15a7841f75872b9a7aca17793532fef2e293", + "num_members": 39, + "raw_bytes": 10636595478, + "cases": [ + "FDG/0001", + "FDG/0002", + "FDG/0003", + "FDG/0004", + "FDG/0005", + "FDG/0006", + "FDG/0007", + "FDG/0008", + "FDG/0009", + "FDG/0010", + "FDG/0011", + "FDG/0012", + "FDG/0013", + "FDG/0014", + "FDG/0015", + "FDG/0016", + "FDG/0017", + "FDG/0018", + "FDG/0019", + "FDG/0020", + "FDG/0021", + "FDG/0022", + "FDG/0023", + "FDG/0024", + "FDG/0025", + "FDG/0026", + "FDG/0027", + "FDG/0028", + "FDG/0029", + "FDG/0030", + "FDG/0031", + "FDG/0032", + "FDG/0033", + "FDG/0034", + "FDG/0035", + "FDG/0036", + "FDG/0037", + "FDG/0038", + "FDG/0039" + ] + }, + { + "name": "data/FDG/FDG_dose_001.tar", + "kind": "dose", + "subset": "FDG", + "compressed": false, + "size_bytes": 10371153920, + "sha256": "5f904c328f2cb386aa6fdd3883586a923cbe856799b03fc9070ea17134842a8f", + "num_members": 48, + "raw_bytes": 10371110643, + "cases": [ + "FDG/0040", + "FDG/0041", + "FDG/0042", + "FDG/0043", + "FDG/0044", + "FDG/0045", + "FDG/0046", + "FDG/0047", + "FDG/0048", + "FDG/0049", + "FDG/0050", + "FDG/0051", + "FDG/0052", + "FDG/0053", + "FDG/0054", + "FDG/0055", + "FDG/0056", + "FDG/0057", + "FDG/0058", + "FDG/0059", + "FDG/0060", + "FDG/0061", + "FDG/0062", + "FDG/0063", + "FDG/0064", + 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b/skills/dosepet-research/SKILL.md @@ -0,0 +1,29 @@ +--- +name: dosepet-research +description: Download selected DosePET dose maps, masks and reports, retrieve and match their source PET/CT images, and prepare verified image pairs and regional data for dosimetry research or model development. +--- + +# DosePET research workflow + +Use this skill for the dataset at [eltzanis/DosePET](https://huggingface.co/datasets/eltzanis/DosePET). The derived release contains 2,100 examinations: 1,503 [18F]FDG, 369 [18F]PSMA-1007 and 228 [68Ga]Ga-PSMA-11. Original PET/CT inputs are retrieved separately from ENHANCE.PET or PSMA-PET-CT-Lesions. Counts describe the manuscript release; inspect the selected repository revision for subsequent changes. + +Keep downloaded inputs intact and put caches, conversions, manifests and modelling outputs in separate directories. Follow the user's requested cases, tracer groups and data types; inspect the download plan before transferring the full collection. Preserve repository revision, source identifiers and file provenance in the outputs. + +## Download and pair images + +Read [references/data-retrieval.md](references/data-retrieval.md) for source links, selective-download commands and matching rules. Obtain the repository's `download_dataset.py`, `shards.json`, and conversion tools from the same revision. Use the supplied download script rather than reconstructing archive-shard membership or assuming every case has an individual Hub folder. + +For regional analyses, start with `metadata/dose_reports.parquet` (one row per examination and structure) or the per-case tables; neither requires image downloads. For image modelling, obtain dose images, relevant masks and case JSON files, then retrieve source PET and CT for those exact examinations. Retain FDG case identifiers including leading zeros. PSMA patient IDs alone are insufficient because patients may have repeated studies; record the patient, examination and CT/PET SeriesInstanceUIDs. Do not substitute another reconstruction, tracer or time point simply because the anatomy looks similar. + +Build a pairing manifest with case_id, tracer, source collection/revision, CT/PET paths, dose path, masks path and available study/series/frame identifiers. Record unresolved or ambiguous matches explicitly and leave them out of model inputs until resolved. Use the metadata's historical source paths as identity clues, not as paths expected to exist on the user's machine. + +## Verify before preparing predictors + +Read [references/dose-semantics.md](references/dose-semantics.md) before interpreting labels or converting them. Run `python tools/convert_dose_images.py check --root ` on downloaded cases: it recomputes every reproducible `dose_report.csv` value from the dose image and masks and prints PASS/FAIL per examination. Confirm each image's origin, orientation, spacing and extent in physical coordinates. CT, PET and dose may have different voxel grids. Shared DICOM StudyInstanceUID/FrameOfReferenceUID supports correspondence but does not establish anatomical registration accuracy. + +Use an identity transform in physical coordinates only when supplied geometry and visual PET/CT/dose overlays support alignment. Inspect matched anatomical landmarks. If anatomy is displaced, report the mismatch and determine whether inputs, motion, registration or dose export is responsible; header equality alone does not resolve it. Do not silently reassign an affine or claim that resampling estimates registration. Use nearest-neighbour interpolation for categorical masks and an appropriate continuous-image interpolator for CT, activity and dose. Save processed copies and document the target grid. + + +DosePET is released under CC BY-NC 4.0: record the licence and the dataset citation given in the repository README in every derived output, and honour the separate terms of the source collections. + +This skill is a recipe for research data preparation. Its existence does not demonstrate successful autonomous modelling, clinical deployment, or prediction accuracy. diff --git a/skills/dosepet-research/agents/openai.yaml b/skills/dosepet-research/agents/openai.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b236d051c2ca32755a386cb64d2671298f4ae1ee --- /dev/null +++ b/skills/dosepet-research/agents/openai.yaml @@ -0,0 +1,4 @@ +interface: + display_name: "DosePET research" + short_description: "Download and pair DosePET with source PET/CT images" + default_prompt: "Use $dosepet-research to download selected DosePET studies and prepare verified source PET/CT-to-dose pairs for research." diff --git a/skills/dosepet-research/references/data-retrieval.md b/skills/dosepet-research/references/data-retrieval.md new file mode 100644 index 0000000000000000000000000000000000000000..356f515e8aa7c1c1c8d96f131a48de58d10f7698 --- /dev/null +++ b/skills/dosepet-research/references/data-retrieval.md @@ -0,0 +1,44 @@ +# Retrieval and examination identity + +## DosePET derived release + +Repository: https://huggingface.co/datasets/eltzanis/DosePET . Licence: CC BY-NC 4.0; follow the repository's reuse terms and cite the dataset as given in its README. Preserve the selected revision and download-helper version. Do not expose access tokens in logs or manifests. + +Retrieve `download_dataset.py` and `shards.json` from that revision. The script selects case content from tar shards; it does not download original PET/CT scans. `PETCT_REVISION` controls its Hub revision, and `PETCT_REPO_ID` controls the repository. Set these to the same revision/repository used to retrieve the helper. Existing scripts accept `--cache` to place caches separately from input or output data. + +Examples from the release helper (run from the directory containing the downloaded script): + +```bash +python download_dataset.py list --cases +python download_dataset.py get --cases FDG/0001 --dry-run --cache ./download-cache --out ./derived-data +python download_dataset.py get --cases FDG/0001 --cache ./download-cache --out ./derived-data +python download_dataset.py get --content tables --cache ./download-cache --out ./regional-data +``` + +For a PSMA case, pass its full `PSMA//` case identifier as listed by the helper; quote it if it contains spaces. `--case-list` accepts a file of case IDs. `--content` accepts `tables`, `dose` and `seg` selections. Avoid replacing the default checksum verification with `--no-verify` without a specific reason. Tables contain all required JSON metadata for modelling selections, but no anatomical inputs or dose images. + +### Regional table in a single file + +`metadata/dose_reports.parquet` holds one row per examination and structure (245,700 rows for the manuscript release) and can be read directly from the Hub, e.g. `pandas.read_parquet("hf://datasets/eltzanis/DosePET/metadata/dose_reports.parquet")`. Its columns are: `case`, `subset`, `organ`, `mean_dose_Gy`, `max_dose_Gy`, `min_dose_Gy`, `dose_std_Gy`, `volume_cm3`, `mass_g`, `num_voxels`, `dcf_fGy_per_Bqs`, `mean_dose_rate_at_scan_mGy_per_s`, `mean_dose_rate_at_scan_mGy_per_h`, `isotope_key`, `half_life_seconds`, `injected_activity_Bq`, `patient_weight_kg`, `injection_time`, `acquisition_time`, `decay_correction`, `cumulated_activity_Bq_s`, `integration_mode`. + +Units differ in one place: the Parquet gives the dose conversion factor in **femtogray** per Bq·s (`dcf_fGy_per_Bqs`), whereas each `dose_report.csv` gives it in gray (`dcf_Gy_per_Bqs`); 1 fGy = 1e-15 Gy. Convert before comparing the two sources. Per-case `dose_report.csv` files also carry the injection-time dose columns where acquisition and injection times were recorded. + +The full current collection is approximately 383 GB compressed and 509 GB unpacked, according to its release README. Check the selected revision and dry-run size rather than rely on those historical numbers. `--all` is useful only when the user's scope calls for the complete dataset. + +## ENHANCE.PET inputs — FDG + +Official record: https://www.scidb.cn/en/detail?dataSetId=c06e548588694eca8477006e806ed70d . Descriptor: https://doi.org/10.1038/s41597-026-07218-y . Use the collection's currently documented download method and source archive index; transient storage URLs are not stable identifiers. + +Source PET/CT volumes are compressed NIfTI. Case `FDG/0001` corresponds to the source CT and PET files with preserved identifier `0001`; verify this against the selected source version and associated `CT-details.xlsx`/`PT-details.xlsx`. Do not infer FDG pairs from clinical attributes or tracer alone. Where selected members are accessible via byte-range archives, use the archive directory/index and source integrity records to extract those exact members; otherwise retrieve the smallest source archive that contains them and extract to a separate input directory. + +Only BQML-eligible source cases are present in DosePET. The ENHANCE.PET source has 1,683 examinations, and DosePET includes 1,503 after excluding 180 CNTS cases. PET values in the supplied NIfTI data have already undergone source conversion; inspect quantitative units and avoid a second DICOM-intensity rescaling. The accompanying spreadsheets and NIfTI geometry provide acquisition and spatial information. + +## PSMA-PET-CT-Lesions inputs + +Official collection: https://www.cancerimagingarchive.net/collection/psma-pet-ct-lesions/ . Descriptor: https://doi.org/10.1038/s41597-026-07821-z . Retrieve the current collection manifest and use TCIA's supported NBIA Data Retriever/API workflow where available. Do not guess a download endpoint or silently agree to new access terms on the user's behalf. + +The DosePET study path retains the source patient ID and examination-folder identity. For example, study suffix `40922` is associated with `PSMA_0bf6ac34fa0c97c0`; neither the suffix nor patient ID alone is a universally sufficient join key. Scan DICOM metadata without pixel decoding to inventory PatientID, StudyInstanceUID, SeriesInstanceUID, Modality, FrameOfReferenceUID and radionuclide fields. Select the intended CT and quantitative PT series from the same study, excluding DICOM SEG objects. Match the study identity against DosePET metadata and available source manifests; document any ambiguous folder-name normalisation. DosePET `metadata.json` does not store DICOM UIDs: for PSMA cases use `patient_id`, the examination folder in the case path, `source_file` (a historical path whose series folder, e.g. `3.000000-PET-…`, names the PET series used) and `num_pet_slices`; obtain the UIDs from the retrieved source DICOM and record them in the pairing manifest. + +Use a DICOM-aware series reader that orders slices by image geometry, rather than lexicographic filenames. Validate the radionuclide, units and reconstruction. F-18 in this PSMA subset identifies [18F]PSMA-1007, while Ga-68 identifies [68Ga]Ga-PSMA-11; F-18 alone does not distinguish a PSMA case from FDG outside that subset. If retrieving source-converted NIfTI instead, require its exact DICOM-series mapping and preserved geometry/units. + +The public PSMA collection notes image processing such as de-facing; verify the source version and field of view rather than assume that every available representation is identical to the production input. Record which files were used for each pair. diff --git a/skills/dosepet-research/references/dose-semantics.md b/skills/dosepet-research/references/dose-semantics.md new file mode 100644 index 0000000000000000000000000000000000000000..788244c97d66ebce03c0855f62c2ebfd2554c6a6 --- /dev/null +++ b/skills/dosepet-research/references/dose-semantics.md @@ -0,0 +1,30 @@ +# Label semantics and conversion + +Typical extracted case layout: + +```text +FDG// or PSMA/// + dose_image.nii.gz + dose_report.csv + metadata.json + dosimetry_config.json + segmentation/.nii.gz + segmentation/statistics.json +``` + +There are 117 mask files/report labels per examination in the manuscript release; some masks are empty. NIfTI dose values, after applying header scaling, are in **Gy**. Integer storage does not make the dose image a segmentation mask. + +These maps integrate dose from scan start to infinity assuming physical decay alone and a fixed spatial activity distribution. They describe the PET radionuclide contribution, not the CT exposure, and do not reconstruct biological redistribution, clearance, full injection-to-infinity dose or therapeutic pharmacokinetics. Use them as simulation-derived reference labels under those assumptions. + +Let `lambda = ln(2)/half_life_seconds` and `A_tilde = cumulated_from_scan` in Bq s. Then: + +- Scan-start dose rate: `dose_Gy * lambda` in Gy/s, or `dose_Gy * lambda * 3.6e6` in mGy/h. +- Whole-source-map dose per decay: `dose_Gy / A_tilde` in Gy/decay. This is conditional on the complete imaged source distribution, not a source-organ-specific S-value or a source-independent transport kernel. + +Resolve half-life from `dosimetry_config.json` if present, otherwise `metadata.json` isotope information. Do not assume identical JSON schemas across the subsets or use injected activity as the represented map-total activity. If constants conflict or are missing, record the case as unresolved rather than invent them. + +Retrieve `tools/convert_dose_images.py` and `tools/README.md` from the selected revision. The supplied `rate` and `dcf` commands write outputs to a separate directory with JSON sidecars; they do not download data. Check the actual helper's `--help` before invocation. Geometry is preserved by scalar conversion, so a conversion cannot correct an existing alignment error. + +Regional mean dose is the arithmetic mean over equal-volume mask voxels. A hollow-organ mask mean should not be called a wall dose. Report spatial dose standard deviation as heterogeneity, distinct from Monte Carlo uncertainty; absent uncertainty is not zero. Empty masks may reflect coverage or segmentation failure, not anatomical absence. Do not interpret minimum/maximum voxel values as independently validated precision. + +When preparing training data, retain the physical grid, quantitative units, source/tracer identity and the temporal interpretation of the target. CT/PET resampling is not registration estimation. Resampling a stored dose map creates a new representation; preserve the original and assess whether interpolation is appropriate for the requested quantitative analysis. Exact native-grid equality is not required for coordinate-based extraction; matching headers alone is insufficient to establish correctly located voxel contents. diff --git a/tools/README.md b/tools/README.md new file mode 100644 index 0000000000000000000000000000000000000000..04bf0b3c6606a394d20d8d13565dc646b19fc347 --- /dev/null +++ b/tools/README.md @@ -0,0 +1,320 @@ +# Dose conversion tools + +`convert_dose_images.py` converts the published total-dose images of the DosePET dataset into: + +- **dose-rate maps**, in mGy/h (or mGy/s, Gy/s), at scan start by default, and +- **dose conversion factor maps**, in **Gy per decay**, + +and can reproduce the shipped per-organ tables from the images and masks so you +can verify the release yourself. + +--- + +## 1. The one thing to know + +Each voxel of `dose_image.nii.gz` is the **total absorbed dose in Gy**, +integrated from the start of the scan to infinity assuming **physical decay +only** (the activity distribution is frozen in space; no biological clearance, +no redistribution). + +Because the only time dependence is a single exponential shared by every voxel, +both conversions are a multiplication of the whole image by **one scalar**: + +| quantity | formula | units | +|---|---|---| +| dose rate at scan start | `Ddot = D * ln2 / T_half` | Gy/s | +| dose rate at scan start | `Ddot = D * 3.6e6 * ln2 / T_half` | mGy/h | +| dose conversion factor | `DCF = D / A_tilde` | Gy per decay | + +where `T_half` is the isotope half-life of that case and `A_tilde` is that +case's cumulated activity in Bq·s (1 Bq·s = 1 decay). Both are recorded per +case inside the dataset — the script finds them for you. + +Two optional time shifts follow from the same relation: + +``` +Ddot(tau) = Ddot(0) * exp(-lambda*tau) tau seconds after the scan +Ddot(t_inj) = Ddot(0) * ICF back to injection time +lambda = ln2 / T_half +``` + +Full derivation and the assumptions to cite: see `../dose_conversion_plan.md`. + +--- + +## 2. Requirements + +```bash +pip install numpy nibabel SimpleITK +``` + +`nibabel` is needed for the image modes, `SimpleITK` for the mask-based modes +(`organs`, `check`). Python 3.8+. + +--- + +## 3. Quick start + +Set a shell variable for the dataset so the examples stay short: + +```bash +DATA="/media/et/Seagate Basic/PET_CT_Dose_Data/dataset_ready_for_publication" +``` + +### 3.1 Dose-rate maps in mGy/h (the common case) + +```bash +python3 convert_dose_images.py rate \ + --root "$DATA/FDG" \ + --out ~/dose_rate_FDG +``` + +Every case gets `dose_rate_at_scan_mGy_per_h.nii.gz` plus a `.json` sidecar +recording the scale factor, half-life, cumulated activity, and the source path. + +Try it on a couple of cases first: + +```bash +python3 convert_dose_images.py rate --root "$DATA/FDG" --out ~/trial --limit 2 --verify +``` + +`--verify` reloads each output and confirms its maximum equals +`max(dose) x k`. + +### 3.2 Other units + +```bash +python3 convert_dose_images.py rate --root "$DATA/PSMA" --out ~/rate_s --unit mGy_per_s +python3 convert_dose_images.py rate --root "$DATA/PSMA" --out ~/rate_gys --unit Gy_per_s +``` + +Choices: `mGy_per_h` (default), `mGy_per_min`, `mGy_per_s`, `Gy_per_s`. + +### 3.3 A different time point (opt-in) + +The default is always **scan start**. To ask for something else: + +```bash +# dose rate one hour after the start of the scan +python3 convert_dose_images.py rate --root "$DATA/FDG" --out ~/rate_1h --at +3600 + +# dose rate back-extrapolated to injection time +python3 convert_dose_images.py rate --root "$DATA/FDG" --out ~/rate_inj --at injection +``` + +`--at injection` uses the stored `injection_correction_factor`. Four FDG cases +(`0718`, `1022`, `1195`, `1289`) do not have one, because the acquisition time +is missing in the source metadata; they are skipped with an explicit message +and everything else proceeds: + +``` + [1/1] SKIP 0718: injection_correction_factor unavailable (acquisition time missing in the source metadata) +``` + +### 3.4 Dose conversion factor maps, Gy per decay + +```bash +python3 convert_dose_images.py dcf --root "$DATA/PSMA" --out ~/dcf_PSMA +``` + +Produces `dose_conversion_factor_Gy_per_decay.nii.gz` per case. + +> **Read this before using DCF.** `A_tilde` is the total cumulated activity in +> the **imaged field of view**. So a DCF voxel means *Gy delivered to that voxel +> per decay occurring anywhere in the FOV, with this patient's own tracer +> distribution as the source*. It is a FOV-normalised S-value. It is **not** +> dose per decay in a specific source organ, and must not be compared directly +> with organ-to-organ S-values from MIRD/OLINDA without that caveat. + +### 3.5 Per-organ tables from the images and masks + +```bash +# one case -> one CSV +python3 convert_dose_images.py organs \ + --case "$DATA/FDG/1597" \ + --out ~/organ_table_1597.csv + +# a whole subset -> one organ_table.csv per case, mirroring the dataset layout +python3 convert_dose_images.py organs --root "$DATA/PSMA" --out ~/organ_tables_PSMA +``` + +The output uses **exactly the column names and order of the published +`dose_report.csv`**, alphabetical by organ, with the same line endings, so you +can diff the two directly: + +```bash +diff <(cut -d, -f1 ~/organ_table_1597.csv) \ + <(cut -d, -f1 "$DATA/FDG/1597/dose_report.csv") # same organs, same order +``` + +One column is written **empty**: `mass_g`, which needs the CT, and the CT is +not part of the release. + +### 3.6 Verify the release against the images + +```bash +python3 convert_dose_images.py check --root "$DATA/FDG" --json-out ~/check_FDG.json +``` + +`check` recomputes every reproducible column from `dose_image.nii.gz` + +`segmentation/*.nii.gz` and compares it against the shipped `dose_report.csv`, +printing an explicit PASS/FAIL per case: + +``` + [1/2] PASS 0001 117 organs, 1283 values, worst 0.499 x tolerance + [2/2] PASS 0002 117 organs, 1284 values, worst 0.496 x tolerance + +2/2 cases PASS, 234 organ rows, 2567 values compared +``` + +"worst 0.499 x tolerance" is the largest deviation found, expressed as a +fraction of that column's own allowance (one quantisation step of the stored +uint16 dose image, carried through the column's scale factor). Anything below +1.0 is agreement. `num_voxels` must match **exactly** and `volume_cm3` to 1e-9 +relative — those two catch a report row paired with the wrong mask. + +To check a single case, point `--root` at that case directory: + +```bash +python3 convert_dose_images.py check --root "$DATA/FDG/0718" +``` + + +--- + +## 4. Output modes, size and precision + +### `--mode header` (default, recommended) + +The dose image is `uint16` with a `scl_slope` in the NIfTI header. Since the +conversion is a pure scalar, the script copies the integer payload **byte for +byte** and multiplies only `scl_slope`: + +``` +scl_slope_out = scl_slope_in * k +``` + +- **No second quantisation**: the output is exactly as precise as the source. + Verified: the uint16 payload of the output is bit-identical to the input's. +- The file stays the same size as the source. +- Residual error is only the float32 rounding of `scl_slope` itself, about + 1e-8 relative — three orders of magnitude below the 1.5e-5 relative + quantisation already present in the published image. + +### `--mode float32` + +Decodes with the slope applied, multiplies, writes float32. Use this if your +software ignores `scl_slope`. Files are roughly 35% larger. + +### Disk space — please read + +Converted maps are the same size as the originals, so converting a whole subset +**duplicates it** (hundreds of GB for FDG). Unless you genuinely need the files +on disk, apply the scalar on the fly instead: + +```python +import json, math, numpy as np, nibabel as nib + +case = "/path/to/dataset/FDG/1597" +cfg = json.load(open(f"{case}/dosimetry_config.json")) +meta = json.load(open(f"{case}/metadata.json")) + +T_half = cfg.get("half_life_seconds") or meta["isotope"]["half_life_seconds"] +A_tilde = cfg["cumulated_from_scan"] + +D = np.asanyarray(nib.load(f"{case}/dose_image.nii.gz").dataobj) # Gy + +rate_mGy_per_h = D * 3.6e6 * math.log(2) / T_half # at scan start +dcf_Gy_per_dec = D / A_tilde # Gy per decay +``` + +And if you only need **organ-level** numbers, you do not need to convert +anything at all: `dose_report.csv` already ships +`mean_dose_rate_at_scan_mGy_per_s`, `mean_dose_rate_at_scan_mGy_per_h` and +`dcf_Gy_per_Bqs`. + +--- + +## 5. Where the constants come from + +Per case, the script resolves the half-life in this order and records which one +it used in the sidecar JSON: + +``` +dosimetry_config.json : half_life_seconds + -> metadata.json : isotope.half_life_seconds + -> metadata.json : dicom_half_life_seconds + -> built-in isotope table (fallback, flagged) +``` + +Cumulated activity comes from `dosimetry_config.json : cumulated_from_scan`, +the same value used to scale the Monte Carlo output. + + +--- + +## 6. Output layout + +``` +<--out>/ + 0001/ + dose_rate_at_scan_mGy_per_h.nii.gz + dose_rate_at_scan_mGy_per_h.json + 0002/ + ... +``` + +For PSMA the nested `//` structure is mirrored. Case discovery +is by searching for `dose_image.nii.gz` at any depth, so the flat FDG layout and +the nested PSMA layout both work with no extra flags. + +Each sidecar JSON records the quantity, units, time point, the scale factor +applied, the formula, the decay-only assumption, the source path, every case +constant with its origin, and the resulting `scl_slope`. + +--- + +## 7. Assumptions and caveats + +1. **Physical decay only.** No biological clearance, no redistribution. All rates and DCFs inherit this. +2. **Time zero is scan start** — the integration window is `scan -> infinity` + in all cases. +3. **`Gy per decay` is FOV-normalised**, not a source-organ S-value (§3.4). +4. **Apply `scl_slope`.** The published images, and the header-mode outputs, are + uint16 with a scale factor in the header. nibabel and SimpleITK do this + automatically; some other toolkits do not. +5. **Four FDG cases have no injection correction factor**: `0718`, `1022`, + `1195`, `1289`. Unaffected for scan-time rates and DCF; only `--at injection` + is unavailable for them. + +--- + +## 8. Safety + +- `--out` is mandatory and the script exits if it resolves inside the dataset: + + ``` + REFUSING to write inside the dataset root. + ``` +- All dataset files are opened read-only. Nothing is written, renamed or + deleted inside the release. +- A case missing a required constant is skipped with a message, never silently + defaulted. + +--- + +## 9. Command reference + +``` +convert_dose_images.py rate --root DIR --out DIR [--unit U] [--at scan|injection|+N] + [--mode header|float32] [--verify] [--limit N] + +convert_dose_images.py dcf --root DIR --out DIR [--mode header|float32] + [--verify] [--limit N] + +convert_dose_images.py organs (--case DIR | --root DIR) --out PATH [--limit N] + +convert_dose_images.py check --root DIR [--json-out FILE] [--limit N] +``` + +`python3 convert_dose_images.py --help` prints the same detail. diff --git a/tools/convert_dose_images.py b/tools/convert_dose_images.py new file mode 100644 index 0000000000000000000000000000000000000000..97520969c7f38877775817a624035116a16aece8 --- /dev/null +++ b/tools/convert_dose_images.py @@ -0,0 +1,860 @@ +#!/usr/bin/env python3 +""" +convert_dose_images.py — turn the published PET/CT total-dose images into +dose-rate maps (mGy/h) and dose-conversion-factor maps (Gy per decay). + +The published `dose_image.nii.gz` of every case holds the TOTAL absorbed dose, +in Gy, obtained by integrating the activity from the start of the scan to +infinity with physical decay only: + + D(r) = integral from t_scan to infinity of Ddot(r,t) dt [Gy] + A(t) = A(t_scan) * exp(-lambda t), lambda = ln2 / T_half + +Because the activity distribution is frozen in space and the only time +dependence is that common exponential, every derived quantity is the dose +image multiplied by ONE scalar per case: + + dose rate at scan start Ddot(r,0) = lambda * D(r) [Gy/s] + = D(r) * 3.6e6 * ln2 / T_half [mGy/h] + + cumulated activity A_tilde = A(t_scan) / lambda [Bq*s] + (1 Bq*s == 1 decay, so A_tilde is a decay count) + + dose conversion factor DCF(r) = D(r) / A_tilde [Gy/decay] + +Optional time shifts, from the same relation: + + Ddot(r,tau) = Ddot(r,0) * exp(-lambda*tau) tau seconds after scan + Ddot(r,t_inj) = Ddot(r,0) * ICF ICF = exp(lambda*dt) + +ASSUMPTION, inherited by everything above: physical decay only. No biological +clearance, no tracer redistribution, no patient motion. + +The script never writes inside the dataset. --out is mandatory and is refused +if it resolves to a path under the dataset root. + +Requires: numpy, nibabel, SimpleITK (SimpleITK only for the mask-based modes). +""" + +from __future__ import annotations + +import argparse +import csv +import gzip +import json +import math +import os +import struct +import sys +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import numpy as np + +# --------------------------------------------------------------------------- +# Constants +# --------------------------------------------------------------------------- + +LN2 = math.log(2.0) + +# Fallback only. Every published case carries its own half-life; this table is +# used solely if all recorded values are missing, and the case is reported. +ISOTOPE_HALF_LIVES = { + "F-18": 6586.2, + "Ga-68": 4062.6, + "Cu-64": 45720.0, + "Lu-177": 574300.0, +} + +RATE_UNITS = { # multiplier applied to a rate in Gy/s + "Gy_per_s": 1.0, + "mGy_per_s": 1.0e3, + "mGy_per_h": 3.6e6, + "mGy_per_min": 6.0e4, +} + +# The column order of the published dose_report.csv, so `organs` output can be +# diffed against it directly. +REPORT_COLUMNS_WITH_INJECTION = [ + "organ", "mean_dose_Gy", "max_dose_Gy", + "mean_dose_injection_Gy", "max_dose_injection_Gy", + "min_dose_Gy", "dose_std_Gy", "volume_cm3", "mass_g", "num_voxels", + "dcf_Gy_per_Bqs", + "mean_dose_rate_at_scan_mGy_per_s", "mean_dose_rate_at_scan_mGy_per_h", +] +REPORT_COLUMNS_NO_INJECTION = [ + c for c in REPORT_COLUMNS_WITH_INJECTION + if c not in ("mean_dose_injection_Gy", "max_dose_injection_Gy") +] + +# Column `organs` mode cannot reproduce from the published files: mass_g needs +# the CT, which is not part of the release, so it is written empty. +UNCOMPUTABLE_COLUMNS = {"mass_g"} + +# NIfTI-1 header field offsets used by the lossless header-only rescaling. +OFF_SIZEOF_HDR = 0 +OFF_DATATYPE = 70 +OFF_SCL_SLOPE = 112 +OFF_SCL_INTER = 116 +OFF_CAL_MAX = 124 +OFF_CAL_MIN = 128 +OFF_DESCRIP = 148 +LEN_DESCRIP = 80 + + +# --------------------------------------------------------------------------- +# Case discovery and per-case constants +# --------------------------------------------------------------------------- + +def find_cases(root: Path) -> List[Path]: + """ + Every directory holding a dose_image.nii.gz, at any depth. This covers the + flat FDG layout (//) and the nested PSMA layout + (///) with no flags. + """ + return sorted({p.parent for p in root.rglob("dose_image.nii.gz")}) + + +def _load_json(path: Path) -> Dict: + try: + with open(path) as fh: + return json.load(fh) + except Exception: + return {} + + +def resolve_case_constants(case_dir: Path) -> Dict: + """ + Collect everything needed to convert one case, and record where each value + came from so the sidecar JSON is self-documenting. + """ + cfg = _load_json(case_dir / "dosimetry_config.json") + meta = _load_json(case_dir / "metadata.json") + + out: Dict = { + "case": str(case_dir), + "isotope_key": meta.get("isotope_key"), + "isotope_name": (meta.get("isotope") or {}).get("name"), + "integration_mode_used": cfg.get("integration_mode_used"), + "errors": [], + "sources": {}, + } + + # --- half-life -------------------------------------------------------- + half_life = None + for value, origin in ( + (cfg.get("half_life_seconds"), "dosimetry_config.json:half_life_seconds"), + ((meta.get("isotope") or {}).get("half_life_seconds"), + "metadata.json:isotope.half_life_seconds"), + (meta.get("dicom_half_life_seconds"), + "metadata.json:dicom_half_life_seconds"), + (ISOTOPE_HALF_LIVES.get(meta.get("isotope_key")), + "built-in isotope table (FALLBACK)"), + ): + if value: + half_life = float(value) + out["sources"]["half_life_seconds"] = origin + break + + if half_life and half_life > 0: + out["half_life_seconds"] = half_life + out["decay_constant_per_s"] = LN2 / half_life + else: + out["errors"].append("no usable half-life found") + + # --- cumulated activity (number of decays, scan -> infinity) ---------- + cumulated = cfg.get("cumulated_from_scan") + if not cumulated: + cumulated = cfg.get("cumulated_activity") + if cumulated: + out["sources"]["cumulated_activity_Bq_s"] = \ + "dosimetry_config.json:cumulated_activity" + else: + out["sources"]["cumulated_activity_Bq_s"] = \ + "dosimetry_config.json:cumulated_from_scan" + + if cumulated and float(cumulated) > 0: + out["cumulated_activity_Bq_s"] = float(cumulated) + else: + out["errors"].append("no usable cumulated activity found") + + # --- injection correction factor (opt-in use only) -------------------- + icf = cfg.get("injection_correction_factor") + if icf and float(icf) > 0: + out["injection_correction_factor"] = float(icf) + out["sources"]["injection_correction_factor"] = \ + "dosimetry_config.json:injection_correction_factor" + + return out + + +def parse_at(spec: str) -> Tuple[str, Optional[float]]: + """ + --at scan -> ("scan", None) dose rate at scan start + --at injection -> ("injection", None) opt-in, needs the stored ICF + --at +3600 / 3600 -> ("offset", 3600.0) seconds after scan start + """ + s = spec.strip().lower() + if s == "scan": + return "scan", None + if s == "injection": + return "injection", None + txt = s[1:] if s.startswith("+") else s + if txt.endswith("s"): + txt = txt[:-1] + try: + seconds = float(txt) + except ValueError: + raise argparse.ArgumentTypeError( + f"--at expects 'scan', 'injection' or a number of seconds " + f"(e.g. +3600); got {spec!r}") + if seconds < 0: + raise argparse.ArgumentTypeError( + "--at with a negative offset is not supported; use 'injection'") + return "offset", seconds + + +def rate_scale_factor(const: Dict, unit: str, at_kind: str, + at_seconds: Optional[float]) -> Tuple[float, str, str]: + """ + The single scalar k with rate = k * D, plus a label and a human-readable + description of the time point. Raises ValueError with a plain message if + the case cannot supply what is needed. + """ + lam = const.get("decay_constant_per_s") + if not lam: + raise ValueError("half-life unavailable") + + k = lam * RATE_UNITS[unit] + + if at_kind == "scan": + return k, f"dose_rate_at_scan_{unit}", "scan start" + + if at_kind == "injection": + icf = const.get("injection_correction_factor") + if not icf: + raise ValueError( + "injection_correction_factor unavailable " + "(acquisition time missing in the source metadata)") + return k * icf, f"dose_rate_at_injection_{unit}", "injection time" + + tau = float(at_seconds or 0.0) + return (k * math.exp(-lam * tau), + f"dose_rate_at_scan_plus_{int(tau)}s_{unit}", + f"{tau:g} s after scan start") + + +def dcf_scale_factor(const: Dict) -> Tuple[float, str, str]: + a_tilde = const.get("cumulated_activity_Bq_s") + if not a_tilde: + raise ValueError("cumulated activity unavailable") + return (1.0 / a_tilde, + "dose_conversion_factor_Gy_per_decay", + "per decay in the imaged field of view") + + +# --------------------------------------------------------------------------- +# Image writing +# --------------------------------------------------------------------------- + +def _read_nifti_bytes(path: Path) -> bytes: + if path.suffix == ".gz": + with gzip.open(path, "rb") as fh: + return fh.read() + return path.read_bytes() + + +def _write_nifti_bytes(path: Path, blob: bytes) -> None: + if path.suffix == ".gz": + # mtime=0 so repeated runs produce byte-identical output + with gzip.GzipFile(filename="", mode="wb", fileobj=open(path, "wb"), + mtime=0) as fh: + fh.write(blob) + else: + path.write_bytes(blob) + + +def convert_header_only(src: Path, dst: Path, k: float, + descrip: str) -> Dict: + """ + Lossless conversion: the uint16 payload is copied byte for byte and only + scl_slope is multiplied by k. Because the conversion is a pure scalar, this + adds NO second quantisation — the output is exactly as precise as the + source — and the file stays the same small size. + + Returns a dict describing what was done, or raises ValueError if the header + cannot represent the new slope (caller falls back to float32). + """ + blob = bytearray(_read_nifti_bytes(src)) + if len(blob) < 348: + raise ValueError("file too short to be a NIfTI-1 image") + + sizeof_hdr_le, = struct.unpack_from("i", blob, OFF_SIZEOF_HDR) + if sizeof_hdr_le == 348: + end = "<" + elif sizeof_hdr_be == 348: + end = ">" + else: + raise ValueError("not a NIfTI-1 header (sizeof_hdr != 348)") + + slope, = struct.unpack_from(end + "f", blob, OFF_SCL_SLOPE) + inter, = struct.unpack_from(end + "f", blob, OFF_SCL_INTER) + if slope == 0.0 or not math.isfinite(slope): + slope = 1.0 # NIfTI: slope 0 means "no scaling" + if not math.isfinite(inter): + inter = 0.0 + + new_slope = slope * k + new_inter = inter * k + as_f32 = struct.unpack(end + "f", struct.pack(end + "f", new_slope))[0] + if as_f32 == 0.0 or not math.isfinite(as_f32): + raise ValueError( + f"scl_slope {new_slope:g} underflows/overflows float32; " + "use --mode float32") + + struct.pack_into(end + "f", blob, OFF_SCL_SLOPE, new_slope) + struct.pack_into(end + "f", blob, OFF_SCL_INTER, new_inter) + + # cal_max / cal_min are display hints; keep them consistent if they were set + for off in (OFF_CAL_MAX, OFF_CAL_MIN): + val, = struct.unpack_from(end + "f", blob, off) + if math.isfinite(val) and val != 0.0: + struct.pack_into(end + "f", blob, off, val * k) + + text = descrip.encode("ascii", "replace")[:LEN_DESCRIP - 1] + struct.pack_into(f"{LEN_DESCRIP}s", blob, OFF_DESCRIP, + text + b"\0" * (LEN_DESCRIP - len(text))) + + dst.parent.mkdir(parents=True, exist_ok=True) + _write_nifti_bytes(dst, bytes(blob)) + return {"mode": "header", "scl_slope_in": slope, + "scl_slope_out": new_slope, "scl_inter_out": new_inter} + + +def convert_float32(src: Path, dst: Path, k: float, descrip: str) -> Dict: + """ + Decode with the slope applied, multiply, write float32. Larger files, but + correct for software that ignores scl_slope. + """ + import nibabel as nib + + img = nib.load(str(src)) + data = np.asanyarray(img.dataobj, dtype=np.float64) * k + out = nib.Nifti1Image(data.astype(np.float32), img.affine, img.header) + out.header.set_data_dtype(np.float32) + out.header.set_slope_inter(1.0, 0.0) + out.header["cal_max"] = 0.0 + out.header["cal_min"] = 0.0 + out.header["descrip"] = descrip.encode("ascii", "replace")[:LEN_DESCRIP - 1] + dst.parent.mkdir(parents=True, exist_ok=True) + nib.save(out, str(dst)) + return {"mode": "float32", "max_value": float(data.max()), + "min_value": float(data.min())} + + +def image_max_value(path: Path) -> float: + import nibabel as nib + img = nib.load(str(path)) + return float(np.asanyarray(img.dataobj, dtype=np.float64).max()) + + +# --------------------------------------------------------------------------- +# Mask statistics (organs / check) +# --------------------------------------------------------------------------- + +def organ_stats(dose_arr: np.ndarray, mask_path: Path, + dose_size, voxel_cm3: float) -> Optional[Dict]: + """ + The statistics of the dose values inside one organ mask — the same thing a + user gets by loading both volumes in a viewer and reading off the region. + """ + import SimpleITK as sitk + + mask_img = sitk.ReadImage(str(mask_path)) + if mask_img.GetSize() != dose_size: + return {"error": f"mask grid {mask_img.GetSize()} != dose grid {dose_size}"} + + mask = sitk.GetArrayFromImage(mask_img).astype(bool) + vals = dose_arr[mask] + if vals.size == 0: + return {"mean_dose_Gy": 0.0, "max_dose_Gy": 0.0, "min_dose_Gy": 0.0, + "dose_std_Gy": 0.0, "num_voxels": 0, "volume_cm3": 0.0} + + return { + "mean_dose_Gy": float(np.mean(vals)), + "max_dose_Gy": float(np.max(vals)), + "min_dose_Gy": float(np.min(vals)), + "dose_std_Gy": float(np.std(vals)), # population std, as shipped + "num_voxels": int(vals.size), + "volume_cm3": float(vals.size) * voxel_cm3, + } + + +def load_dose(case_dir: Path): + import SimpleITK as sitk + img = sitk.ReadImage(str(case_dir / "dose_image.nii.gz")) + arr = sitk.GetArrayFromImage(img).astype(np.float64) + sx, sy, sz = img.GetSpacing() + return img, arr, sx * sy * sz / 1000.0 + + +def compute_organ_table(case_dir: Path, const: Dict) -> Tuple[List[Dict], Dict]: + """ + Per-organ table with the same column names and order as the published + dose_report.csv, so the two files diff directly. + """ + seg_dir = case_dir / "segmentation" + masks = sorted(p for p in seg_dir.glob("*.nii.gz")) + img, dose_arr, voxel_cm3 = load_dose(case_dir) + size = img.GetSize() + + lam = const.get("decay_constant_per_s") + a_tilde = const.get("cumulated_activity_Bq_s") + icf = const.get("injection_correction_factor") + + rows: List[Dict] = [] + info = {"quant_step_Gy": float(dose_arr.max()) / 65535.0, + "num_masks": len(masks), "mask_errors": []} + + for mask_path in masks: + organ = mask_path.name[:-len(".nii.gz")] + st = organ_stats(dose_arr, mask_path, size, voxel_cm3) + if st is None or "error" in st: + info["mask_errors"].append((organ, st.get("error") if st else "unreadable")) + continue + + row = {c: "" for c in REPORT_COLUMNS_WITH_INJECTION} + row["organ"] = organ + for key in ("mean_dose_Gy", "max_dose_Gy", "min_dose_Gy", + "dose_std_Gy", "volume_cm3", "num_voxels"): + row[key] = st[key] + + if icf: + # the published columns are rounded to 8 decimals at this step + row["mean_dose_injection_Gy"] = round(st["mean_dose_Gy"] * icf, 8) + row["max_dose_injection_Gy"] = round(st["max_dose_Gy"] * icf, 8) + + if a_tilde: + row["dcf_Gy_per_Bqs"] = st["mean_dose_Gy"] / a_tilde + if lam: + rate_gy_s = st["mean_dose_Gy"] * lam + row["mean_dose_rate_at_scan_mGy_per_s"] = rate_gy_s * 1.0e3 + row["mean_dose_rate_at_scan_mGy_per_h"] = rate_gy_s * 3.6e6 + + rows.append(row) + + rows.sort(key=lambda r: r["organ"]) # alphabetical, as published + return rows, info + + +def write_organ_table(rows: List[Dict], dst: Path, with_injection: bool) -> None: + columns = (REPORT_COLUMNS_WITH_INJECTION if with_injection + else REPORT_COLUMNS_NO_INJECTION) + dst.parent.mkdir(parents=True, exist_ok=True) + # newline="\n" matches the line endings of the published dose_report.csv, + # so the two files can be compared with a plain `diff` + with open(dst, "w", newline="\n") as fh: + writer = csv.DictWriter(fh, fieldnames=columns, extrasaction="ignore", + lineterminator="\n") + writer.writeheader() + for row in rows: + writer.writerow({c: row.get(c, "") for c in columns}) + + +# --------------------------------------------------------------------------- +# check +# --------------------------------------------------------------------------- + +def check_case(case_dir: Path) -> Dict: + """ + Recompute every reproducible column from dose_image.nii.gz + segmentation/ + and compare against the shipped dose_report.csv. + + Agreement is expected to within one quantisation step of the stored uint16 + dose image, propagated through each column's own scale factor. + """ + result = {"case": str(case_dir), "status": "ok", "rows": 0, + "columns_checked": 0, "worst": [], "notes": []} + + const = resolve_case_constants(case_dir) + report = case_dir / "dose_report.csv" + if not report.exists(): + result.update(status="fail", notes=["dose_report.csv missing"]) + return result + + with open(report) as fh: + published = {r["organ"]: r for r in csv.DictReader(fh)} + + rows, info = compute_organ_table(case_dir, const) + recomputed = {r["organ"]: r for r in rows} + + step = info["quant_step_Gy"] + lam = const.get("decay_constant_per_s") or 0.0 + a_tilde = const.get("cumulated_activity_Bq_s") or 1.0 + icf = const.get("injection_correction_factor") or 1.0 + + # tolerance per column: one quantisation step carried through that column's + # own scale factor (+ 5e-9 where the published value was rounded to 8 dp) + tol = { + "mean_dose_Gy": step, "max_dose_Gy": step, + "min_dose_Gy": step, "dose_std_Gy": step, + "mean_dose_injection_Gy": step * icf + 5e-9, + "max_dose_injection_Gy": step * icf + 5e-9, + "dcf_Gy_per_Bqs": step / a_tilde, + "mean_dose_rate_at_scan_mGy_per_s": step * lam * 1.0e3, + "mean_dose_rate_at_scan_mGy_per_h": step * lam * 3.6e6, + "volume_cm3": 0.0, # relative check below + "num_voxels": 0.0, # must match exactly + } + + only_published = sorted(set(published) - set(recomputed)) + only_recomputed = sorted(set(recomputed) - set(published)) + if only_published: + result["notes"].append( + f"{len(only_published)} CSV rows without a mask: {only_published[:5]}") + if only_recomputed: + result["notes"].append( + f"{len(only_recomputed)} masks without a CSV row: {only_recomputed[:5]}") + for organ, err in info["mask_errors"]: + result["notes"].append(f"mask {organ}: {err}") + result["status"] = "fail" + + failures: List[Tuple] = [] + worst: List[Tuple] = [] + + for organ in sorted(set(published) & set(recomputed)): + pub, rec = published[organ], recomputed[organ] + result["rows"] += 1 + for column, limit in tol.items(): + raw = pub.get(column, "") + mine = rec.get(column, "") + if raw in ("", None) or mine in ("", None): + continue + try: + reported = float(raw) + except ValueError: + continue + mine = float(mine) + result["columns_checked"] += 1 + diff = abs(mine - reported) + + if column == "num_voxels": + if diff != 0: + failures.append((organ, column, reported, mine, diff)) + continue + if column == "volume_cm3": + rel = diff / max(abs(reported), 1e-30) + if rel > 1e-9: + failures.append((organ, column, reported, mine, diff)) + continue + + allowed = limit + 1e-9 * max(abs(reported), abs(mine), 1.0) + ratio = diff / allowed if allowed > 0 else 0.0 + worst.append((ratio, organ, column, reported, mine, diff, allowed)) + if diff > allowed: + failures.append((organ, column, reported, mine, diff)) + + worst.sort(reverse=True) + result["worst"] = [ + {"organ": o, "column": c, "published": p, "recomputed": m, + "difference": d, "tolerance": a, "fraction_of_tolerance": r} + for r, o, c, p, m, d, a in worst[:5] + ] + result["failures"] = [ + {"organ": o, "column": c, "published": p, "recomputed": m, "difference": d} + for o, c, p, m, d in failures[:20] + ] + result["num_failures"] = len(failures) + result["quant_step_Gy"] = step + if failures: + result["status"] = "fail" + return result + + +# --------------------------------------------------------------------------- +# Safety +# --------------------------------------------------------------------------- + +def guard_output(out: Path, root: Optional[Path]) -> Path: + """ + Refuse to write anywhere inside the dataset. The published tree is + read-only as far as this tool is concerned. + """ + out = out.expanduser().resolve() + if root is not None: + root = root.expanduser().resolve() + if out == root or root in out.parents: + sys.exit(f"REFUSING to write inside the dataset root.\n" + f" --root {root}\n --out {out}\n" + f"Choose an --out outside the dataset.") + return out + + +# --------------------------------------------------------------------------- +# Commands +# --------------------------------------------------------------------------- + +def _case_label(case_dir: Path, root: Path) -> str: + try: + label = str(case_dir.relative_to(root)) + except ValueError: + return case_dir.name + # --root pointed straight at one case directory + return case_dir.name if label == "." else label + + +def cmd_convert(args, kind: str) -> int: + root = Path(args.root).expanduser().resolve() + out_root = guard_output(Path(args.out), root) + cases = find_cases(root) + if not cases: + sys.exit(f"no dose_image.nii.gz found under {root}") + if args.limit: + cases = cases[:args.limit] + + if kind == "rate": + try: + at_kind, at_seconds = parse_at(args.at) + except argparse.ArgumentTypeError as exc: + sys.exit(str(exc)) + else: + at_kind, at_seconds = "scan", None + + print(f"{len(cases)} case(s) under {root}") + print(f"writing to {out_root} (mode: {args.mode})\n") + + written = skipped = failed = 0 + for i, case_dir in enumerate(cases, 1): + label = _case_label(case_dir, root) + const = resolve_case_constants(case_dir) + try: + if kind == "rate": + k, stem, when = rate_scale_factor(const, args.unit, + at_kind, at_seconds) + units = args.unit.replace("_per_", "/") + else: + k, stem, when = dcf_scale_factor(const) + units = "Gy/decay" + except ValueError as exc: + print(f" [{i}/{len(cases)}] SKIP {label}: {exc}") + skipped += 1 + continue + + dst = out_root / label / f"{stem}.nii.gz" + descrip = f"{units} at {when}; k={k:.6g} x Gy" + src = case_dir / "dose_image.nii.gz" + + try: + if args.mode == "header": + try: + detail = convert_header_only(src, dst, k, descrip) + except ValueError as exc: + print(f" [{i}/{len(cases)}] {label}: header mode " + f"unavailable ({exc}); writing float32") + detail = convert_float32(src, dst, k, descrip) + else: + detail = convert_float32(src, dst, k, descrip) + except Exception as exc: # noqa: BLE001 + print(f" [{i}/{len(cases)}] FAIL {label}: {exc}") + failed += 1 + continue + + sidecar = { + "produced_by": "convert_dose_images.py", + "quantity": ("dose rate" if kind == "rate" + else "dose conversion factor"), + "units": units, + "time_point": when, + "scale_factor_applied": k, + "formula": ("rate = D * ln2 / T_half" + + ("" if at_kind == "scan" else + (" * ICF" if at_kind == "injection" + else " * exp(-lambda*tau)")) + if kind == "rate" else "DCF = D / cumulated_activity"), + "assumption": ("physical decay only: activity distribution frozen " + "in space, no biological clearance, no " + "redistribution, no patient motion"), + "source_dose_image": str(src), + "case_constants": const, + "conversion": detail, + } + if kind == "dcf": + sidecar["note"] = ( + "Gy per decay occurring anywhere in the imaged field of view, " + "with this patient's own tracer distribution as the source. " + "This is a FOV-normalised S-value, not dose per decay in a " + "specific source organ.") + with open(dst.with_suffix("").with_suffix(".json"), "w") as fh: + json.dump(sidecar, fh, indent=2) + + if args.verify: + expected = image_max_value(src) * k + got = image_max_value(dst) + rel = abs(got - expected) / max(abs(expected), 1e-30) + flag = "ok" if rel < 1e-5 else f"MISMATCH rel={rel:.2e}" + print(f" [{i}/{len(cases)}] {label} k={k:.6g} max={got:.6g} " + f"{units} verify:{flag}") + if rel >= 1e-5: + failed += 1 + continue + else: + print(f" [{i}/{len(cases)}] {label} k={k:.6g}") + written += 1 + + print(f"\nwritten {written}, skipped {skipped}, failed {failed}") + return 1 if failed else 0 + + +def cmd_organs(args) -> int: + if args.case: + cases = [Path(args.case).expanduser().resolve()] + root = cases[0].parent + else: + root = Path(args.root).expanduser().resolve() + cases = find_cases(root) + if args.limit: + cases = cases[:args.limit] + out = guard_output(Path(args.out), root if args.root else None) + + single = len(cases) == 1 and args.case is not None + for i, case_dir in enumerate(cases, 1): + const = resolve_case_constants(case_dir) + rows, info = compute_organ_table(case_dir, const) + with_injection = "injection_correction_factor" in const + dst = out if single else out / _case_label(case_dir, root) / "organ_table.csv" + write_organ_table(rows, dst, with_injection) + note = "" if with_injection else " (no ICF: injection columns omitted)" + print(f" [{i}/{len(cases)}] {_case_label(case_dir, root)}: " + f"{len(rows)} organs -> {dst}{note}") + for organ, err in info["mask_errors"]: + print(f" mask {organ}: {err}") + print(f"\nmass_g is left empty: it needs the CT, which is not part of " + f"the release.") + return 0 + + +def cmd_check(args) -> int: + root = Path(args.root).expanduser().resolve() + cases = find_cases(root) + if not cases: + sys.exit(f"no dose_image.nii.gz found under {root}") + if args.limit: + cases = cases[:args.limit] + + print(f"checking {len(cases)} case(s) under {root}") + print("Each line states PASS or FAIL for that case, the number of organ " + "rows and\nvalues compared, and the worst deviation as a fraction of " + "its own tolerance.\n") + + results = [] + n_fail = 0 + for i, case_dir in enumerate(cases, 1): + res = check_case(case_dir) + results.append(res) + verdict = "PASS" if res["status"] == "ok" else "FAIL" + worst = res["worst"][0]["fraction_of_tolerance"] if res["worst"] else 0.0 + extra = "" + if res.get("num_failures"): + f0 = res["failures"][0] + extra = (f" first: {f0['organ']}/{f0['column']} " + f"published {f0['published']:.6g} vs {f0['recomputed']:.6g}") + print(f" [{i}/{len(cases)}] {verdict} {_case_label(case_dir, root)} " + f"{res['rows']} organs, {res['columns_checked']} values, " + f"worst {worst:.3f} x tolerance{extra}") + for note in res["notes"]: + print(f" note: {note}") + if res["status"] != "ok": + n_fail += 1 + + total_rows = sum(r["rows"] for r in results) + total_vals = sum(r["columns_checked"] for r in results) + print(f"\n{len(results) - n_fail}/{len(results)} cases PASS, " + f"{total_rows} organ rows, {total_vals} values compared") + if n_fail == 0: + print("Every reproducible column of every organ agrees with the " + "published dose_report.csv,\nwithin one quantisation step of the " + "stored uint16 dose image.") + if args.json_out: + with open(args.json_out, "w") as fh: + json.dump(results, fh, indent=2) + print(f"details written to {args.json_out}") + return 1 if n_fail else 0 + + +# --------------------------------------------------------------------------- +# CLI +# --------------------------------------------------------------------------- + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="convert_dose_images.py", + description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + sub = parser.add_subparsers(dest="command", required=True) + + def add_common(sp, need_out=True): + sp.add_argument("--root", required=True, + help="dataset subset root, e.g. .../FDG or .../PSMA") + if need_out: + sp.add_argument("--out", required=True, + help="output directory (must be OUTSIDE the dataset)") + sp.add_argument("--limit", type=int, default=0, + help="process only the first N cases (for a trial run)") + + p_rate = sub.add_parser( + "rate", help="write dose-rate maps (default: mGy/h at scan start)") + add_common(p_rate) + p_rate.add_argument("--unit", choices=sorted(RATE_UNITS), + default="mGy_per_h") + p_rate.add_argument( + "--at", default="scan", + help="time point: 'scan' (default), 'injection' (opt-in, needs the " + "stored injection correction factor), or seconds after the scan " + "such as +3600") + p_rate.add_argument("--mode", choices=("header", "float32"), + default="header") + p_rate.add_argument("--verify", action="store_true", + help="reload each output and confirm its maximum") + + p_dcf = sub.add_parser( + "dcf", help="write dose-conversion-factor maps, Gy per decay") + add_common(p_dcf) + p_dcf.add_argument("--mode", choices=("header", "float32"), default="header") + p_dcf.add_argument("--verify", action="store_true") + + p_org = sub.add_parser( + "organs", + help="per-organ table from dose image + masks, in the exact column " + "order of the published dose_report.csv") + group = p_org.add_mutually_exclusive_group(required=True) + group.add_argument("--case", help="a single case directory") + group.add_argument("--root", help="a dataset subset root, for batch mode") + p_org.add_argument("--out", required=True, + help="output CSV (with --case) or directory (with --root)") + p_org.add_argument("--limit", type=int, default=0) + + p_chk = sub.add_parser( + "check", + help="recompute the published columns from image + masks and compare") + add_common(p_chk, need_out=False) + p_chk.add_argument("--json-out", help="write full details to this JSON file") + + return parser + + +def main(argv=None) -> int: + args = build_parser().parse_args(argv) + if args.command == "rate": + return cmd_convert(args, "rate") + if args.command == "dcf": + return cmd_convert(args, "dcf") + if args.command == "organs": + return cmd_organs(args) + if args.command == "check": + return cmd_check(args) + return 2 + + +if __name__ == "__main__": + sys.exit(main())